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Under the current water crisis in agriculture, irrigation methods for saving and conserving water are necessary. However, these methods must guarantee an appropriate yield with a concomitant economic benefit and a reduced environmental impact. In this study, two irrigation thresholds for irrigation timing (IT) based on thermal imagery were analyzed with the UNICA potato variety in three trials under drip (DI) and furrow (FI) irrigation during 2017–2018 in Lima, Peru. The control (T1) remained at >70% of soil field capacity. For other treatments, thresholds were defined based on stomatal conductance at light saturation (T2: 0.15 and T3: 0.05 mol H 2 O m − 2 s − 1 ) and crop water stress index (T2: 0.4 and T3: 0.6) based on canopy temperature. An integrated index (IIN) was established for the valuation of treatments using the criteria of high fresh tuber yield (FTY) and a low total amount of irrigated water, production cost (PC), and total C emissions (TE) and using criteria of a score. FI-T2 (0.69–0.72) and DI-T3 (0.19–0.29) showed the highest and lowest IIN value, respectively. FTY in T2 was not significantly reduced under FI, resulting in a lower PC regarding DI–T2 and emphasizing the usefulness of thermal imagery in determining watering schedules in potatoes under furrow irrigation systems. [ABSTRACT FROM AUTHOR]

Light use efficiency (LUE) models have been widely used to estimate terrestrial gross primary production (GPP). However, the estimation of GPP still has large uncertainties owing to an insufficient understanding of the complex relationship between water availability and photosynthesis. The plant water stress index (PWSI), which is based on canopy temperature, is very sensitive to the plant stomatal opening and has been regarded as a good indicator for monitoring plant water status at the regional scale. In this study, we selected a cork oak plantation in northern China with an obvious seasonal drought as the research object. Using the ground-observed data, we evaluated the applicability of the LUE models with typical water stress scalars (MOD17, MODTEM, EC-LUE, ECM-LUE, SM-LUE, GLO-PEM, and Wang) in a GPP simulation of the cork oak plantation and explored whether the model's accuracy can be improved by applying PWSI to modify the above models. The results showed that among the seven LUE models, the water stress scalar had a greater impact on the model's performance than the temperature stress scalar. On sunny days, the daily GPP simulated by the seven LUE models was poorly matched with the measured GPP, and all models explained only 23–52% of the GPP variation in the cork oak plantation. The modified LUE models can significantly improve the prediction accuracy of the GPP and explain 49–65% of the variation in the daily GPP. On cloudy days, the performance of the modified LUE models did not improve, and the evaporative fraction was more suitable for defining the water stress scalar in the LUE models. The ECM-LUE and the modified GLO-PEM based on PWSI had optimal model structures for simulating the GPP of the cork oak plantation under cloudy and sunny days, respectively. This study provides a reference for the accurate prediction of GPP in terrestrial ecosystems in the future. [ABSTRACT FROM AUTHOR]

The crop water stress index (CWSI) is a widely used analytical tool based on portable thermography. This method can be useful in replacing the traditional stem water potential method obtained with a Scholander chamber (PMS Model 600) because the latter is not feasible for large-scale studies due to the time involved and the fact that it is invasive and can cause damage to the plant. The present work had three objectives: (i) to understand if CWSI estimated using an aerial sensor can estimate the water status of the plant; (ii) to compare CWSI from aerial-thermographic and portable thermal cameras with stem water potential; (iii) to estimate the capacity of an unmanned aerial vehicle (UAV) to calculate and spatialize CWSI. Monitoring of CWSI (CWSIP) using a portable device was performed directly in the canopy, by measuring reference temperatures (Tdry, Twet, and canopy temperature (Tc)). Aerial CWSI calculation was performed using two models: (i) a simplified CWSI model (CWSIS), where the Tdry and Twet were estimated as the average of 1% of the extreme temperature, and (ii) an air temperature model (CWSITair) where air temperatures (Tair + 7 °C) were recorded as Tdry and in the Twet, considering the average of the lowest 33% of histogram values. In these two models, the Tc value corresponded to the temperature value in each pixel of the aerial thermal image. The results show that it was possible to estimate CWSI by calculating canopy temperatures and spatializing CWSI using aerial thermography. Of the two models, it was found that for CWSITair, CWSIS (R2 = 0.55) evaluated crop water stress better than stem water potential. The CWSIS had good correlation compared with the portable sensor (R2 = 0.58), and its application in field measurements is possible. [ABSTRACT FROM AUTHOR]

Plant-based measurements such as leaf water potential (LWP) are widely used for irrigation scheduling because they are accurate at indicating when irrigation is needed. Despite being a good indicator, scheduling irrigation with LWP is time consuming and scale-limited. The work reported in this study explored the potential of using thermal remote sensing to estimate cotton crop water status in the humid southeastern U.S.A. The study was conducted over two growing seasons (2018 and 2020) in southwestern Georgia, U.S.A using a complete randomized block design plot scheme with three irrigation treatments (0% ETc (crop evapotranspiration; rainfed), 100% ETc (well-irrigated), and 125% ETc (over-irrigated). To monitor the irrigation treatment effects on cotton physiological responses, predawn LWP (LWPPD), stomatal conductance (gs) and leaf area index (LAI) data were collected in both growing seasons. UAV-based images collected in the thermal infrared waveband were used to calculate crop water stress index (CWSI) based on three different methodologies and evaluated as predictors of LWPPD. Results in this study suggest that LWPPD values above -0.45 MPa indicate a non-stressed crop. No negative effects in leaf stomatal conductance and crop growth were observed in 2018. In 2020, the less and more irregular precipitation led to significant differences in LAI and gs, as well as in LWPPD. A moderate to strong relationship was observed for all dates in 2020, with the CWSI based on the Monteith approach (CWSIMonteith) showing the two highest R2 values among the 3 dates (0.65 and 0.58) with low RMSE values of 0.02 and 0.04 MPa, respectively. Overall, the results showed that there is potential of using an affordable UAV-based thermal system to produce predicted LWP maps that are representative of the current field water status.

Using thermal infrared remote sensing from unmanned aerial vehicles (UAVs) to obtain crop canopy temperature and calculate the crop water stress index (CWSI) is a promising method for monitoring field water conditions. However, such endeavors are often constrained to instantaneous scales due to the diurnal variability of thermal infrared data. To address this limitation, we developed a daily-scale CWSI suitable for UAV remote sensing, enhancing the temporal representativeness of crop water stress diagnostics. We focused on spring maize in the Hetao Irrigation District of Inner Mongolia and investigated four key growth stages. UAV thermal infrared was used to obtain multiple instantaneous statistical CWSI (CWSI s) values during the day. UAV multispectral data and the Penman–Monteith model were combined to obtain the actual evapotranspiration and daily-scale CWSI (CWSI t_day). A temporal upscaling model from instantaneous CSWI to daily-scale CWSI was established by comparing the relationships between the CWSI s and CWSI t_day at different times. Results show that compared to the fluctuations of the CWSI s values throughout the day, those of the CWSI t_day values were smaller, with values of 0.13, 0.09, 0.03, and 0.03 during the ninth leaf (V9), tasseling (VT), silking (R1), and milk (R3) stages, respectively. The CWSI t_day demonstrated a higher correlation with the measured stomatal conductance (g s) at different time periods, thereby being more stable and temporally representative. However, both indices may incorrectly interpret the decline in leaf physiological activity due to aging as water stress at the end of maize growth, leading to overestimated CWSI values. The temporal upscaling model, which was developed by combining CWSI s values observed at 12:00, 14:00, and 16:00 with the random forest regression algorithm, achieved coefficient of determination of 0.794 and root mean square error of 0.04. Hence, multiple instantaneous observations can be used effectively instead of daily-scale observations, providing key insights into the popularization and application of the CWSI t_day. Overall, this study presents a new method for obtaining continuous CWSI values with high temporal and spatial resolutions based on a UAV platform. [Display omitted] • A daily scale CWSI (CWSI t_day) suitable for UAV remote sensing was developed. • CWSI t_day offers greater stability and temporal representativeness for assessing crop water stress. • Multiple instantaneous-scale CWSI (CWSI s) observations can effectively replace CWSI t_day observations. • CWSI s and CWSI t_day obtained based on UAV can misdiagnose the moisture state of maize at the end of its growth. [ABSTRACT FROM AUTHOR]

An efficient management of water relies on the correct estimation of tree water requirements and the accurate monitoring of tree water status and canopy growth. This study aims to test the suitability of visible and thermal images acquired by an unmanned aerial vehicle (UAV) for monitoring tree water status and canopy growth in an irrigation experiment. We used mature olive trees of two cultivars subjected to full irrigation, deficit irrigation (41–44% of full irrigation), or rainfed conditions. Deficit irrigation had limited or no effect on fruit and oil yield. There was a significant relationship between the remotely sensed crop water stress index derived from thermal images and the stem water potential (R2 = 0.83). The RGB images by UAV allowed to estimate tree canopy volume and were able to detect differences in canopy growth across irrigation regimes. A significant relationship between canopy volume and LAI was found for both cultivars (R2 of 0.84 and 0.88 for Frantoio and Leccino, respectively). Our results confirm the positive effects of deficit irrigation strategies to save relevant volumes of water and show that aerial images from UAV can be used to monitor both tree water stress and its effects on canopy growth and yield. [ABSTRACT FROM AUTHOR]

Drought has been identified as a major factor restricting maize productivity worldwide, especially in the rainfed areas. The objective of the present study was to investigate the physiological adaptation strategies and sugar-related gene expression levels in three maize (Zea mays L.) genotypes with different drought tolerance abilities (Suwan4452, drought tolerant as a positive check; S7328, drought susceptible as a negative check; Pac339, drought susceptible) at the seedling stage. Ten-day old seedlings of maize genotypes were subjected to (i) well-watered (WW) or control and (ii) water-deficit (WD) conditions. Leaf osmotic potential of cv. S7328 under WD was significantly decreased by 1.35–1.45 folds compared with cv. Pac339 under WW, whereas it was retained in cv. Suwan4452, which utilized total soluble sugars as the major osmolytes for maintaining leaf greenness, Fv/Fm, ΦPSII, and stomatal function (Pn, net photosynthetic rate; gs, stomatal conductance; and E, transpiration rate). Interestingly, sucrose degradation (65% over the control) in cv. Pac339 under WD was evident in relation to the downregulation of the ZmSPS1 level, whereas glucose enrichment (1.65 folds over the control) was observed in relation to the upregulation of ZmSPS1 and ZmSUS1. Moreover, CWSI (crop water stress index), calculated from leaf temperature of stressed plants, was negatively correlated with E, gs, and Pn. Overall, growth characteristics, aboveground and belowground parts, in the drought-susceptible cv. Pac339 and cv. S7328, were significantly decreased (> 25% over the control), whereas these parameters in the drought-tolerant cv. Suwan4452 were unaffected. The study validates the use of leaf temperature, CWSI, Pn, gs, and E as sensitive parameters and overall growth characters as effective indices for drought tolerance screening in maize genotypes at the seedling stage. However, further experiments are required to validate the results observed in this study under field conditions. [ABSTRACT FROM AUTHOR]

The objectives of the current study were to implement affordable and non-invasive measurements of infrared thermometry, leaf relative water content (RWC), crop water stress index (CWSI), leaf area index (LAI) from computer vision analysis and seed yield of sunflowers. The experiment was designed as split-plot based on randomized complete blocks with three replications. Treatments were four different levels of deficit irrigation as the main plots and three fertilization treatments were applied as sub-plots. Results showed a significant effect (P ≤ 0.01) of water stress and fertilizer on CWSI during different stages of sunflower growth. Changes in fertilizer amount and type resulted in a change in lower (dTLL) and upper (dTUL) limits of canopy-air temperature difference. A combination of chemical fertilizer with biofertilizer could help to decrease CWSI. From computer vision analysis, the normalized difference red blue index (NDRBI) had a strong linear relationship with RWC and CWSI for sunflowers (R2 of 0.87 and 0.93, respectively) and the normalized difference green blue index (NDGBI) had a linear relationship with seed yield (R2 = 0.79). Therefore, analysis of digital RGB images and CWSI were efficient, non-destructive and low-cost methods to assess crop water status for sunflowers under different irrigation and fertilizer treatments. HIGHLIGHTS The CWSI values were sensitive not only to different irrigation regimes but also to amount and type of fertilizer.; The CWSI can be derived from the plant index (NDRBI) and used for appropriate irrigation scheduling.; A positive correlation was observed between image indices with CWSI and LAI.; Combination of chemical fertilizer with biofertilizer could help to decrease CWSI.;

Due to the limited availability of Root-Zone Soil Moisture (RZSM) information at the regional scale, this paper explores the use of thermal infrared remote sensing to estimate RZSM in agricultural fields. This study presents the Crop Water Stress Index (CWSI) derived from thermal infrared data used as an indicator to estimate root zone soil moisture. Theoretical limits were calculated using canopy and air temperature difference, which is related to vapor pressure deficit. An empirical model was developed using continuous remotely sensed optical, thermal infrared data with limited meteorological data collected from a wheat site, the Dookie experimental farm, Victoria, Australia during 2012 and 2013. Linear and exponential models predicting RZSM using CWSI were constructed and compared in two different cropping seasons. Cross-validation results demonstrate that the linear model predicted RZSM with an error of 3.9% in 2012 and 5.3% in 2013 cropping seasons. The proposed method is applied to another root-zone soil moisture dataset collected during 2002–04 cropping seasons from a corn field site in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site in the USA. Validation results showed that the model produces reasonable RZSM estimates, except for the high rainfall distribution during cropping seasons even though the crop types of the calibration and validation sites were different. The efficacy of canopy temperature in RZSM estimations was demonstrated using Dookie and OPE3 RZSM dataset. The potential limitation is that sparse vegetation in the initial growth stages produces negative values in CWSI due to the dominant soil surface. Overall, the results support the potential role of the theoretical crop water stress index in root-zone soil moisture estimations.

Water productivity of rice is imperative for global water security. Currently, water saving management techniques have been proposed and applied to rice systems. The crop water stress index (CWSI) is a major index for evaluating crop water use. The utility of the CWSI in rice in a humid temperate climate has been given little attention. Previous studies have focused on upland crops and readily available constant reference baselines, primarily the water stressed baseline (WSB), which does not inherently reflect transpiration flux. This study examined the performance of the estimated non-water stressed baseline (NWSB) and WSB for rice in a humid climate and the CWSI sensitivity under variable reference baseline scenarios in a 2-year pot trial under phytotron and field environment conditions with two rice genotypes (IRAT109 and Takanari) in a flooded (FL) and aerobic (AR) water regime. We observed that the dynamics of CWSI is dependent not only on the water regimes but could be strongly influenced by genotype sensitivity to vapor pressure deficit (VPD). A higher slope (pooled data) in the field environment (−5.68 °C kPa−1) compared to the phytotron (−3.04 °C kPa−1) reflected transpiration water loss sensitivity to VPD thresholds. Further studies with diverse rice germplasms to explore generalizability to field conditions and reformulation of reference baselines considering the VPD threshold sensitivity could prove to be significant. [ABSTRACT FROM AUTHOR]

Drought is an abiotic stress that inhibits plant growth and development and, therefore, declines crop productivity, as seen in maize plant. The aim of this investigation was to identify the candidate maize varieties that can be grown under water limited conditions using physio-morphological and yield attributes. Eight genotypes of maize including ‘Suwan4452’ (drought tolerant) as a positive check, ‘CP301’, ‘CP-DK888’, ‘DK7979’, ‘DK9901’, ‘Pac339’, ‘S7328’, and ‘Suwan5’ were selected as test plants. Physiological, biochemical and morphological characteristics at seedling (24 day after sowing; DAS) and reproductive (80 DAS) developmental stages of plants under 20-day water withholding (WD), and yield traits at harvesting period were analysed. Leaf temperature in each genotype increased with the degree of water deficit stress, leading to leaf chlorosis, and reduction in maximum quantum yield of PSII (Fv/Fm), photon yield of PSII (ΦPSII), net photosynthetic rate (Pn), overall growth and yield. Pn and stomatal conductance (gs) in drought tolerant genotype, ‘Suwan4452’, were decreased by 19.1% and 18.6%, respectively, whereas these in drought sensitive, ‘Pac339’, were significantly declined by 53.9% and 61.8%, respectively. Physio-morphological parameters, growth performance and yield-related traits of maize genotypes grown under water deficit conditions and well-watered conditions were subjected to Ward’s cluster method for identification of water deficit tolerant cultivars. Maintaining photosynthetic abilities, osmotic adjustment and CWSI in drought tolerant genotypes of maize were evidently demonstrated to keep overall growth performance and yield attributes. Based on multivariate cluster analysis and PCA (principal component analysis), ‘Suwan4452’, ‘CP-DK888’ and ‘S7328’ were categorized as drought tolerant genotypes whereas ‘Suwan5’, ‘Pac339’, ‘DK7979’, ‘CP301’ and ‘DK9901’ were identified as drought susceptible cultivars. Hybrid maize cvs. ‘CP-DK888’ and ‘S7328’ may further be suggested to be grown in the rainfed area without irrigation.

The goal of this study is to determine the crop water stress index (CWSI) and irrigation scheduling based on CWSI values, as well as to examine the correlations between CWSI, physiological parameters and grain yield of hybrid corn P31A34 in semi-arid climate conditions. In 2014 and 2015, the upper limit (UL) temperatures at which plants were entirely exposed to water stress were 1.178°C and 2.38°C, respectively. When the corn grain yield began to decline, the CWSI threshold value was 0.34, indicating the yield limit. Grain yield, crop water consumption, crop water stress index, chlorophyll content, water use efficiency and leaf area index were found to have negative correlations (p = 0.01) with CWSI values in both years of the study. The findings revealed that in semi-arid climate conditions, a maximum of 30% water deficit could be used during the growing period of the corn compared to full irrigation (I100) for water savings and that a water deficit greater than 30% results in considerable grain yield losses. In areas with limited water resources, the moderate water deficit (I70) may be a viable alternative to the I100. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) with thermal infrared remote sensing have been used to rapidly extract the temperature of the crop canopy, further to monitor the water stress condition of the crop. The removal of soil background from the thermal infrared images can be an effective way to improve the monitoring accuracy of crop moisture. But there is also a great challenge on the thermal infrared image processing. In this study, an accurate and rapid temperature extraction was implemented to monitor the water stress using the UAVs with remote sensing images. The summer maize at the jointing stage was selected as the research object. There were also four completely irrigation treatments with three replicates and a total of 12 experimental plots. Specifically, the four water treatments were: T1 (50% of the field capacity), T2 (65% of the field capacity), T3 (80% of the field capacity), and T4 (95%-100% of the field capacity). The thermal infrared and visible images were captured by the UAVs on July 26, July 27, July 29, July 31, and August 2, 2020. The data of canopy temperature and soil water content at 20cm depth were also collected at the same time. After that, the Otsu algorithm, EXG-Kmeans, and Otsu-EXG-Kmeans were selected to determine the canopy area in the images. The accuracy of the extraction was evaluated from four aspects, including the schematics of canopy area, classification accuracy, temperature histogram, and correlation with the measured canopy temperature. An optimal combination of parameters was achieved to extract the thermal infrared images of canopy temperature. Subsequently, a crop water stress index (CWSI) was also achieved during this time. The relationship between CWSI and soil water content was then established to monitor the water deficit of maize, according to the daily average trend of CWSI. The results showed that the Otsu-EXG-Kmeans presented a higher extraction accuracy for the canopy temperature (User's accuracy: 95.9%>83.2%>66.6%), and the extracted canopy temperature was closer to the measured temperature (r:0.788>0.762>0.750>0.737), indicating that the Otsu-EXG-Kmeans was an effective way to accurately extract the canopy temperature. The CWSI presented a higher correlation with the soil water content (r:-0.738<-0.666), compared with the canopy temperature. The daily average variation trend of CWSI was more consistent with the actual situation. Consequently, the improved extraction can be widely expected to extract the canopy temperature, thereby accurately monitoring the water shortage situation of maize. This finding can provide a strong reference for the UAVs to accurately monitor the crop water stress under remote sensing. [ABSTRACT FROM AUTHOR]

Current trends in the intensification of olive cultivation including irrigation have dictated a need to reconsider amendment management practices. The addition of organic wastes to agricultural soils is becoming a common practice as a disposal strategy and to improve the physical and chemical soil properties This study examines physiological varieties of young olive trees (Olea europaea L.) grown on deficit irrigation under four different amended soil. We evaluated the short-term effect of different amended soil under deficit irrigation on soil, plant water status, level of soil, and leaf Nitrogen. Water stress was considered to be insignificant or absent with an irrigation amount of 50% of daily evapotranspiration. Leaf relative water content was between 65 and 88% for the Chemlali cultivar, while that of the Koroneiki was between 60 and 70%. Under conditions of water stress, photosynthesis of trees planted in soil amended with biochar decreased from 8.5 to 1.5 μmol.CO2.m−2s−1, compared to the control for both cultivars.To conclude, amendment addition, used as a tool for sustainable agriculture, could improve plant growth, although soil fertility. The type and amount of amendment should receive special attention to be efficient. [ABSTRACT FROM AUTHOR]

• Crop physiology sensing system was modified with an edge-compute algorithm. • Developed was an algorithm for real-time apple crop water stress monitoring. • Crop water stress index (CWSI) was estimated using segmented shaded canopy. • CWSI was highly correlated with mid-day stem water potential (r = -0.80) • Apple heat mitigation techniques substantially affected algorithms performance. • Estimations were robust under fogging (r = -0.76) compared to other techniques. Elevated air temperature (>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p < 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards. [ABSTRACT FROM AUTHOR]

• Cotton water status was estimated using vegetation indices and textural information. • Combining vegetation indices with textural information enhanced estimation accuracy. • The XGBoost_VIs + TFs + TIs model performed the best in estimating CWSI. • The estimated CWSI predictive map could diagnose cotton canopy water stress. Precise and timely estimation of crop canopy water status is of great importance for precision irrigation. The unmanned aerial vehicle (UAV)-based remote sensing technology has become increasingly popular for crop canopy water status estimation. Nevertheless, the capability of vegetation indices (VIs) along with textural information from high-resolution imagery for estimating cotton canopy water status has been rarely explored. The VIs, texture features (TFs), and texture indices (TIs) were obtained from UAV multispectral images of cotton with different irrigation levels and nitrogen rates in the southern Xinjiang of China. The performances of three machine learning models, i.e. support vector machine (SVM), back-propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were evaluated for estimating canopy equivalent water thickness (CEWT) and crop water stress index (CWSI) throughout the cotton growing season. The results showed that combining vegetation indices and textural information significantly improved the estimation accuracy of models compared to vegetation indices or textural information alone. The XGBoost_VIs + TFs model exhibited the best accuracy in estimating CEWT (R2 = 0.75, RMSE = 0.01 cm, RE = 19.46 % at upper half-canopy level, and R2 = 0.65, RMSE = 0.02 cm, RE = 24.59 % at all-canopy level), while the XGBoost_VIs + TFs + TIs model performed best in predicting CWSI among the models (R2 = 0.90, RMSE = 0.05, RE = 5.84 %). Although CEWT estimation was fair to some extent, CWSI estimation was more applicable for diagnosing cotton water stress. The CWSI maps created from the optimal XGBoost_VIs + TFs + TIs model intuitively reflected the cotton canopy water status under various irrigation levels and nitrogen rates, which could help farmers implement timely and precision irrigation in cotton production. [ABSTRACT FROM AUTHOR]

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Aluminum (Al) toxicity in acidic soils is a major problem in rice crop production, especially in the acid sulfate soil (pH < 4.0). Selecting Al-tolerant varieties of rice with low toxicity is one of the most appropriate strategies to overcome this problem. In the present study, we investigated the Al content in different rice genotypes, IR64 (high yielding), RD35 (local acidic-tolerant), and Azucena (AZU, positive-check Al-tolerant), and their physiological and morphological adaptations under a wide range Al (10, 25, 50 mM [Al2(SO4)3]) treatments in the greenhouse conditions. Under 50-mM Al treatment, Al levels in the root tissues of rice seedlings cvs. AZU and IR64 were increased by 2.74- and 2.10-fold over control. Interestingly, Al contents in the roots of cv. RD35 were also exhibited by 2.04-fold over control. Similarly, Al contents in the leaves trend to increase in relation to a degree of Al treatments, leading to increase leaf temperature, chlorophyll degradation, limited CO2 assimilation, and negative effect on root traits under 50 mM Al were evidently observed. Therefore, leaf temperature was considered a sensitive parameter regulated by high concentration of Al (50 mM), leading to increase in crop water stress index (CWSI > 0.6) and decrease in stomata conductance. Net photosynthetic rate (Pn) and transpiration rate (E) in rice seedlings of cv. RD35 subjected to 50 mM Al were significantly dropped by 74.76% and 47.71% over the control, respectively, resulting in reduced growth performances in terms of root length (26.57% reduction) and shoot fresh weight (46.15% reduction). An enrichment of Al in the root tissues without toxicity in rice cv. AZU may further help in discovering the Al homeostasis. In summary, Al enrichment in rice genotypes grown under Al-treatments was evidently observed in the root, leading to the limited root growth, root length, and root dry weight, especially in cv. RD35. Al restriction in the root tissues of cv. AZU (Al-tolerant) may play a key role as defense mechanisms to avoid translocation to other organs and the stomata closure was an alternative key factor to limit H2O transpiration. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) can help farmers to monitor their crops and provide irrigation and inputs as and when the crops need, reducing risks to yields. This study uses UAV imagery to measure water and plant stress in the winter wheat fields, lying in high, medium and low Desertification Vulnerability Indexed (DVI) zones of South Punjab, a region that has an agrarian economy subject to severe desertification. UAV flights were conducted in nine wheat fields in three districts of Bahawalpur, Rahim Yar Khan and Rajanpur. Flights were operated at 15 m altitude above ground level at midday, February 2019, presenting good resolution images of 30.48ppi, in RGB, with a pixel depth of 16 Bit, from a DJI Phantom 3 Standard quadcopter. Dronedeploy was used for image pre-processing and generating orthomosaics of the nine fields. Orthomosaics were uploaded on the Agremo app, where water stress and plant stress analysis of the sampled fields was performed. Agremo generated maps were reclassified in Arc Map 10.5. Fatehpur Union Council, lying in the High DVI zone, was found to suffer most severe plant stress, potential plant stress, and water stress with 34.83%, 51.16% and 42.35% of the crop affected respectively. The sample fields in high DVI zones in two of the three study districts suffered the highest amounts of plant stress and water stress. The conclusions offer guidance to policy makers on where water redistribution may need to be considered so that exacerbating desertification risk can be avoided, particularly in the most vulnerable zones. [ABSTRACT FROM AUTHOR]

The lower limit temperature in the crop water stress index (CWSI) model refers to the canopy temperature ( T c ) or the canopy-air temperature differences ( dT ) under well-watered conditions, which has significant impacts on the accuracy of the model in quantifying plant water status. At present, the direct estimation of lower limit temperature based on data-driven method has been successfully used in crops, but its applicability has not been tes-ted in forest ecosystems. We collected continuously and synchronously T c and meteorological data in a Quercus variabilis plantation at the southern foot of Taihang Mountain to evaluate the feasibility of multiple linear regression model and BP neural network model for estimating the lower limit temperature and the accuracy of the CWSI indicating water status of the plantation. The results showed that, in the forest ecosystem without irrigation conditions, the lower limit temperature could be obtained by setting soil moisture as saturation in the multiple linear regression mo-del and the BP neural network model with soil water content, wind speed, net radiation, vapor pressure deficit and air temperature as input parameters. Combining the lower limit temperature and the upper limit temperature determined by the theoretical equation to normalize the measured T c and dT could realize the non-destructive, rapid, and automatic diagnosis of the water status of Q. variabilis plantation. Among them, the CWSI obtained by combining the lower limit temperature determined by the dT under well-watered condition calculated by the BP neural network model and the upper limit temperature was the most suitable for accurate monitoring water status of the plantation. The coefficient of determination, root mean square error, and index of agreement between the calculated CWSI and measured CWSI were 0.81, 0.08, and 0.90, respectively. This study could provide a reference method for efficient and accurate monitoring of forest ecosystem water status.

Water deficit stress is a major factor that inhibits the overall growth and development in cassava (Manihot esculenta), leading to decreased storage root yield. We conducted a study to investigate whether thermal sensing could be used to indicate water deficit stress and the health and yield of cassava crops in field. The objective of the study was to use thermal imaging to determine relationship between crop water stress index (CWSI) and physiological changes, and to identify the critical CWSI point in fields of cassava cv. Rayong 9 under well-irrigated and water-deficit conditions. At the time of storage root initiation (85 DAP [day after planting]), thermal imagery was collected and the physiological changes and growth characters were measured prior to storage root harvesting (162 DAP). Thermal infrared imager was used to measure the canopy temperature and CWSI of cassava plants. Net photosynthetic rate (Pn), stomatal conductance (gs) and transpiration rates (Tr) of cassava plants under water deficit conditions for 29 d (114 DAP) were significantly decreased, leading to delayed plant growth as compared to those under well-irrigated conditions. In contrast, air vapor pressure deficit (VPDair) and CWSI in drought-stressed plants were higher than well irrigated plants. High correlations between Tr/gs/Pn and CWSI were observed. The study concludes that CWSI is a sensitive indicator of water deficit stress caused due to stomatal function. Abbreviations: CWSI: crop water stress index; DAP: day after planting; Pn: net photosynthetic rate; gs: stomatal conductance; Tr: transpiration rate; VPDair: air vapor pressure; RMSE: root mean square error

The present research was carried out to determine water-stress tolerance of linoleic sunflower genotypes (P64LE119, PR63F73, P64LL62) grown under different water stress conditions [no water-stress (I100); mild water-stress (I70); strong water-stress (I35)] in the years 2015 and 2016. Variance analyses revealed significant differences between the genotypes (P

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This paper deals with the possibility of using infrared thermography to determine the irrigation requirement for a given field depending on the water stress level of the plants. The experiments were performed on vines at the site in the village of Jadrtovac near Šibenik, Croatia. The water stress level of plants was determined through leaf temperature measurements and measurements of leaf water potential (LWP). Leaf temperature measurements were performed using infrared thermography. Based on these measurements the relationship between the LWP and a derived factor, Crop Water Stress Index (CWSI) was investigated. The results obtained show that infrared thermography could be successfully used to measure the leaf temperature, which is the input parameter for calculating the CWSI. [ABSTRACT FROM AUTHOR]

Precise irrigation management of grapevines in greenhouses requires a reliable method to easily quantify and monitor the grapevine water status to enable effective manipulation of the water stress of the plants. This study evaluated the applicability of crop water stress index (CWSI) based on the leaf temperature for diagnosing the grapevine water status. The experiment was conducted at Yuhe Farm (northwest China), with drip-irrigated grapevines under three irrigation treatments. Meteorological factors, soil moisture contents, leaf temperature, growth indicators including canopy coverage and fruit diameter, and physiological indicators including SPAD (relative chlorophyll content), stem water potential (φs), stomatal conductance (φs), and transpiration rate (E) were studied during the growing season. The results show that the relationship between the leaf-air temperature difference (Tc-Ta) and the plant water status indicators (φs, gs, E) were significant (P < 0.05), and the relationship between gs, E and Tc-Ta was the closest, with R² values ranging from 0.530-0.604 and from 0.545-0.623, respectively. CWSI values are more easily observed on sunny days, and it was determined that 14:00 BJS is the best observation time for the CWSI value under different non-water-stressed baselines. There is a reliable linear correlation between the CWSI value and the soil moisture at 0-40 cm (P < 0.05), which could provide a reference when using the CWSI to diagnose the water status of plants. Compared with the Tc-Ta value, the CWSI could more accurately monitor the plant water status, and above the considered indictors, gs has the greatest correlation with the CWSI. [ABSTRACT FROM AUTHOR]

Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adoption in production agriculture. Development of generalized crop-specific upper and lower reference temperatures is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data-driven models for predicting the reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed from measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over five years under full and severe deficit irrigation. Lower reference temperatures (TLL) were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1°C. The relationship between TLL minus ambient air temperature and vapor pressure deficit was represented with a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures that were nearly double the values reported in previous studies. A daily CWSI, calculated as the average of 15 min CWSI values between 13:00 and 16:00 MDT for sugarbeet and between 13:00 and 15:00 local time for wine grape, were well correlated with irrigation events and amounts. There was a significant (p < 0.001) linear relationship between the daily CWSI and midday leaf water potential of Malbec and Syrah wine grapes, with an R2 of 0.53. The data-driven models developed in this study to estimate reference temperatures enable automated calculation of the CWSI for effective assessment of crop water stress. However, measurements taken under conditions of wet canopy or low solar radiation should be disregarded as they can result in irrational values of the CWSI. [ABSTRACT FROM AUTHOR]

Irrigation scheduling based on the crop water stress index (CWSI) and temperature-time threshold (TTT) methods is promising for semi-arid and arid climates. The objective of this study was to investigate if CWSI and TTT methods could be used as irrigation signaling tools for a humid environment in the southeastern U.S. Corn canopy temperature data were collected in Alabama in 2018 and 2019 using infrared leaf temperature sensors on a fully irrigated treatment and on two limited irrigation treatments. A set of three soil water sensors installed at 0.15, 0.3, and 0.6 m soil depth were used to prescribe irrigation time and amount. CWSI was sensitive to precipitation, irrigation, and plant water uptake. No statistical differences in CWSI or yield among the three irrigation levels were found in 2018 when precipitation was well distributed during the season. In contrast, during 2019 both CWSI and yield differed significantly among the three irrigation treatments. Precipitation events in 2019 were sparse compared to 2018; therefore, irrigation promoted greater differences in water availability between treatments. Inconsistencies observed in potential irrigation signaling using the TTT method with or without the inclusion of a limiting relative humidity algorithm indicate that the TTT method may not be a reliable irrigation signaling tool for humid environments. [ABSTRACT FROM AUTHOR]

• Irrigation and nutrient management are key factors for a desirable yield of potato;. • Water use efficiency and potassium were linked to the productivity of potato;. • Infrared thermometry-based stress indices help to predict potato yield under moisture stress;. • Stress monitoring tools are useful as non-destructive tools in the production system of potato. As the demand for food continues to rise and water availability challenges intensify, the delicate equilibrium between efficient water usage and sustainable agricultural production has become increasingly pivotal. Recognizing the significance of water stress monitoring and nutrient management in the country's agricultural landscape, two years of field trials (2018–2019 and 2019–2020) were conducted at the Regional Research Station, Bidhan Chandra Krishi Viswavidyalaya, Gayeshpur, Nadia, to demonstrate the efficiency of canopy temperature-based indices for monitoring crop water stress in potato (cv. Kufri Jyoti) under varied irrigation (IW:CPE = 0.4, 0.8, 1.2 and 1.6) and potassium doses (K 2 O @ 100, 150 and 200 kg ha−1). The seasonal evapotranspiration of potato plants ranged from 121.0 mm to 320.9 mm during the crop growth period, and it exhibited a quadratic relationship with both the total dry biomass and the tuber yield. In 2019–2020, the highest water use efficiency (WUE) of 12.74 kg m−3 was obtained from the application of IW; CPE= 1.20 with the application of K 2 O @ 200 kg ha−1. In the following year, the highest WUE of 9.78 kg m−3 was obtained from irrigation at IW:CPE= 1.60, along with K 2 O application @ 200 kg ha−1. During a major part of the growth period, the canopy air temperature difference (CATD) was greater in the treatments in which irrigation was applied at IW:CPE = 0.4 and 0.8 than in those in which irrigation was applied at higher levels, i.e., IW:CPE = 1.2 and 1.6, implying the effectiveness of CATD as a stress indicator. The stress indices, stress degree days (SDD) and crop water stress index (CWSI) accumulated over the crop growing season showed decreasing trends with increasing irrigation and potassium doses. Both the accumulated SDD and accumulated CWSI showed a strong inverse relationship with tuber yield. The significant linear relationship between tuber yield and the seasonal accumulation of both infrared thermometry-based stress indices (i.e., SDD and CWSI) may help to predict crop yield in response to seasonal moisture stress and thus aid in strategic crop planning under the limited availability of irrigation. [ABSTRACT FROM AUTHOR]

To optimize crop water consumption and adopt water-saving measures such as precision irrigation, early identification of plant water status is critical. This study explores the effectiveness of estimating water stress in choy sum (Brassica chinensis var. parachinensis) grown in pots in greenhouse conditions using Crop Water Stress Index (CWSI) and crop vegetation indicators to improve irrigation water management. Data on CWSI and Spectral reflectance were collected from choy sum plants growing in sandy loam soil with four different soil field capacities (FC): 90–100% FC as no water stress (NWS); 80–90% FC for light water stress (LWS); 70–80% FC for moderate water stress (MWS); and 60–70% FC for severe water stress (SWS). With four treatments and three replications, the experiment was set up as a completely randomized design (CRD). Throughout the growing season, plant water stress tracers such as leaf area index (LAI), canopy temperature (Tc), leaf relative water content (LRWC), leaf chlorophyll content, and yield were measured. Furthermore, CWSI estimated from the Workswell Wiris Agro R Infrared Camera (CWSIW) and spectral data acquisition from the Analytical Spectral Device on choy sum plants were studied at each growth stage. NDVI, Photochemical Reflectance Index positioned at 570 nm (PRI570), normalized PRI (PRInorm), Water Index (WI), and NDWI were the Vegetation indices (VIs) used in this study. At each growth stage, the connections between these CWSIW, VIs, and water stress indicators were statistically analyzed with R2 greater than 0.5. The results revealed that all VIs were valuable guides for diagnosing water stress in choy sum. CWSIW obtained from this study showed that Workswell Wiris Agro R Infrared Camera mounted on proximal remote sensing platform for assessing water stress in choy sum plant was rapid, non-destructive, and user friendly. Therefore, integrating CWSIW and VIs approach gives a more rapid and accurate approach for detecting water stress in choy sum grown under greenhouse conditions to optimize yield by reducing water loss and enhancing food security and sustainability.

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

The main objectives of this study are to assess crop water stress index (CWSI) and to determine irrigation schedule of pomegranate using infrared thermometer technique. Treatments with three replications were irrigated every three and six days intervals, as much as 0.50 (S1), 0.75 (S2), 1.00 (S3), and 1.25 (S4) of Class A pan evaporation. Additionally, an unirrigated plot was also formed. The equations obtained for non-water stress baseline is Tc-Ta=0.645VPD-4.338. The equation of full water stressed upper baseline obtained from data taken from branches of pomegranate plant cut in different days is given by Tc-Ta= 0.0489VPD+2.8734. According to the results obtained, it is concluded that infrared thermometer can be used for irrigation scheduling of the pomegranate plants in Antalya conditions. When using this technique in Antalya conditions, it is suggested to keep the seasonal mean CWSI value approximately 0.12 and the index value of 0.40 can be used to start the irrigation. Additionally, it is suggested that the amount of irrigation can be determined as much as the amount of evaporation measured until the index value reaches 0.40.

Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found ( rPearson = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha−1 ), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m³ ha−1 in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings [ABSTRACT FROM AUTHOR]

This research was conducted as a field study to determine effects on yield and yield parameters of sunflower plants and plant water stres index of deficit irrigation with drip irrigation applications of Siirt climate conditions. Irrigation issues in research; 1 full irrigation (I100 control subject) and full irrigation 80% (I20), 60% (I40), 40% (I60) and 20% (I80) rate cuts made four irrigation and is formed from no irrigation water is not applied I0. It made a statistically significant relationship between the yield and yield parameters were determined as a result of statistical analysis. Effect on yield and yield components of irrigation was significant at the 1% level and has been affected at different levels. Depending on the applied water shortages and climatic conditions, evapotranspiration of sunflower plants, plant water stres index, chlorophyll and yield is greatly affected. CWSI highest value during the vegetation period 0.39, I0 the lowest value in the CWSI for irrigation is set at 0.19 in fully irrigated I100. In the study of sunflower grain yield began to decline, the CWSI threshold value determined from the previous infraret thermometer observations from irrigation has been identified as 0.29. According to the study's results made it is not recommended to do or make cuts in Siirt deficit irrigation on sunflower must have if it is the vegetative stage (before flowering) is made up of 20% is recommended. [ABSTRACT FROM AUTHOR]

Low radiation is one of the most important factors which limits potential yield in potato. Under humid conditions, the dominance of diffuse radiation not only imposes challenges for radiation use efficiency in crops but also limits the water status surveillance through non-invasive methods like infrared radiometry. This study was carried out in the humid desert of the Peruvian central coast with the aim to relate maximum stomatal conductance (gs_max, an important water status indicator) with leaf and air temperature (dT) and crop water stress index (CWSI). In a potted trial, gs_max vs. dT were compared along the day in well-irrigated (field capacity) and water restricted (half field capacity) plants. In an additional field experiment, CWSI was validated by testing two irrigation timing treatments with pre-established gs_max threshold (0.15 [T1] and 0.50 [T2] mol H2O m−2 s−1) against a control (frequently irrigated). An acute stomatal closure sensitivity was detected which drove a gs_max fall (gs↓) near the solar noon. The intense stomatal closure caused a dT rise which showed positive higher values (> 2 °C) after gs↓. The significant yield reduction of T1 in relation to the control (− 38.2 ± 10.7%) highlighted that gs_max values > 0.15 must be used to warrant a high potato yield. These findings support the use of CWSI values ≤ 0.3–0.4 as thresholds for an appropriate irrigation in potatoes with assessments taken at around 15:00 hours, time in which plants have accumulated enough radiation allowing an appropriate detection of thermal emission under humid conditions. [ABSTRACT FROM AUTHOR]

Highlights • Comprehensive Stress Indicator and stem water potential were reasonably correlated. • Comprehensive Stress Indicator is derived from energy balance of a leaf. • Comprehensive Stress Indicator does not depend on a lower baseline. Abstract This study evaluated a comprehensive plant water stress indicator that integrates the leaf temperature and the environmental conditions that can provide feedback on the plant response to irrigation scheduling. This quantity—Comprehensive Stress Indicator (CSI)—is based on the reformulation of the leaf energy balance equation. Specifically, CSI is the ratio of the temperature difference between a dry leaf (i.e., a leaf with a broken stem) and a live leaf (on the same tree) [i.e., T dry -T leaf ] and the difference between the vapor pressure inside the stomatal cavity at saturation and the vapor pressure of the air at ambient temperature [i.e., e s (T L)-e(T A)]. The required measurements to compute CSI include dry leaf temperature, live leaf temperature, relative humidity, and air temperature at the tree being monitored. In this study, all measurements were obtained using a sensor suite called the leaf monitor. Leaf monitors were connected to a wireless mesh network to communicate the data to a website. The leaf monitor included two thermal infrared sensors, one for the dry leaf and one for the live leaf, both of which were housed in the same unit with almost identical environmental conditions due to a diffusing hemispherical dome that enclosed these sensors. The CSI is a dynamic indicator with a value for every data sample collected. For a single indicator representing each day, the CSI was evaluated in two ways. First, the CSI was integrated with respect to time from 10 A.M. to 6 P.M. to obtain the Integrated Comprehensive Stress Indicator (ICSI). Second, the CSI was averaged from 1 P.M. to 3 P.M. to obtain the Average Comprehensive Stress Indicator (ACSI). Both ICSI and ACSI were compared to other stress indicators, including the existing Crop Water Stress Index (CWSI) and Integrated Degrees Above Non-Stressed (IDANS). Results show that ICSI and ACSI are satisfactorily correlated with midday stem water potential (SWP). The ICSI and ACSI indicators may be more convenient than other stress indicators because they require measurements only at the tree being monitored. [ABSTRACT FROM AUTHOR]

The present research was carried out to determine water-stress tolerance of malting barley genotypes (Durusu. Firat. Atilir) grown under (winter planting), different water conditions [no water-stress (I1oo); strong water-stress (Io)] in the years 2016 and 2017. Variance analyses revealed significant differences between the genotypes (P<0.01). As the average of two years, the greatest yield was obtained from no water-stress x genotype interaction (IiooxDurusu) with 5613.50 kg ha-1, the lowest yield was obtained from strong water stress x genotype interaction (Iox Atilir) with 3848.82 kg ha-1. Again as the average of two years, the greatest chlorophyll content was obtained from no water-stress x genotyrpe interaction (IooxDmnsu) with 45.23 spad. the lowest value was obtained from strong water stress x genotype interaction (Iox Atilir) with 35.80 spad. The greatest crop water stress index was obtained from strong water stress x genotype interaction (Iox Atilir) with 0.47. the lowest value was obtained from no water-stress x genotype interaction (I1ooxDurusu) with 0.24. The lowest protein content was ootameci irom no water-stress x genotype interaction (Ioox Diunsu) with 9.36 %, the greatest value was obtained from strong water stress x genotype interaction (IoxAtilir) with 12.98 %. The Dumsu genotype with optimum water use efficiency and prominent with crop water stress index, chlorophyll, chlorophyll content and low protein content both in no water-stress and strong water stress treatments was identified as water stress-resistant and the genotype was considered to have reliable characteristics potentially to be used in further water stress-resistance studies. On tell other hand Atilir (malting barley) genotype was identified as water stress-sensitive. As a result decreased protein content and total protein content in plants under no water-stress treatments. [ABSTRACT FROM AUTHOR]

Precise irrigation management of grapevines in greenhouses requires a reliable method to easily quantify and monitor the grapevine water status to enable effective manipulation of the water stress of the plants. This study evaluated the applicability of crop water stress index (CWSI) based on the leaf temperature for diagnosing the grapevine water status. The experiment was conducted at Yuhe Farm (northwest China), with drip-irrigated grapevines under three irrigation treatments. Meteorological factors, soil moisture contents, leaf temperature, growth indicators including canopy coverage and fruit diameter, and physiological indicators including SPAD (relative chlorophyll content), stem water potential (φs), stomatal conductance (gs), and transpiration rate (E) were studied during the growing season. The results show that the relationship between the leaf-air temperature difference (Tc-Ta) and the plant water status indicators (φs, gs, E) were significant (P < 0.05), and the relationship between gs, E and Tc-Ta was the closest, with R2 values ranging from 0.530–0.604 and from 0.545–0.623, respectively. CWSI values are more easily observed on sunny days, and it was determined that 14:00 BJS is the best observation time for the CWSI value under different non-water-stressed baselines. There is a reliable linear correlation between the CWSI value and the soil moisture at 0–40 cm (P < 0.05), which could provide a reference when using the CWSI to diagnose the water status of plants. Compared with the Tc-Ta value, the CWSI could more accurately monitor the plant water status, and above the considered indictors, gs has the greatest correlation with the CWSI.

In viticulture, detailed spatial information about actual evapotranspiration (ETa) and vine water status within a vineyard may be of particular utility when applying site-specific, precision irrigation management. Over recent decades, extensive research has been carried out in the use of remote sensing energy balance models to estimate and monitor ETa at the field level. However, one of the major limitations remains the coarse spatial resolution in the thermal infrared (TIR) domain. In this context, the recent advent of the Sentinel missions of the European Space Agency (ESA) has greatly improved the possibility of monitoring crop parameters and estimating ETa at higher temporal and spatial resolutions. In order to bridge the gap between the coarse-resolution Sentinel-3 thermal and the fine-resolution Sentinel-2 shortwave data, sharpening techniques have been used to downscale the Sentinel-3 land surface temperature (LST) from 1 km to 20 m. However, the accurate estimates of high-resolution LST through sharpening techniques are still unclear, particularly when intended to be used for detecting crop water stress. The goal of this study was to assess the feasibility of the two-source energy balance model (TSEB) using sharpened LST images from Sentinel-2 and Sentinel-3 (TSEB-PTS2+3) to estimate the spatio-temporal variability of actual transpiration (T) and water stress in a vineyard. T and crop water stress index (CWSI) estimates were evaluated against a vine water consumption model and regressed with in situ stem water potential (Ψstem). Two different TSEB approaches, using very high-resolution airborne thermal imagery, were also included in the analysis as benchmarks for TSEB-PTS2+3. One of them uses aggregated TIR data at the vine+inter-row level (TSEB-PTairb), while the other is based on a contextual method that directly, although separately, retrieves soil and canopy temperatures (TSEB-2T). The results obtained demonstrated that when comparing airborne Trad and sharpened S2+3 LST, the latter tend to be underestimated. This complicates the use of TSEB-PTS2+3 to detect crop water stress. TSEB-2T appeared to outperform all the other methods. This was shown by a higher R2 and slightly lower RMSD when compared with modelled T. In addition, regressions between T and CWSI-2T with Ψstem also produced the highest R2.

The midday stem water potential (Ψs) and stomatal conductance (gs) have been traditionally used to monitor the water status of cherry trees (Prunus avium L.). Due to the complexity of direct measurement, the use of infrared thermography has been proposed as an alternative. This study compares Ψs and gs against crop water stress indexes (CWSI) calculated from thermal infrared (TIR) data from high-resolution (HR) and low-resolution (LR) cameras for two cherry tree cultivars: ‘Regina’ and ‘Sweetheart’. For this purpose, a water stress–recovery cycle experiment was carried out at the post-harvest period in a commercial drip-irrigated cherry tree orchard under three irrigation treatments based on Ψs levels. The water status of trees was measured weekly using Ψs, gs, and compared to CWSIs, computed from both thermal cameras. Results showed that the accuracy in the estimation of CWSIs was not statistically significant when comparing both cameras for the representation of Ψs and gs in both cultivars. The performance of all evaluated physiological indicators presented similar trends for both cultivars, and the averaged differences between CWSI’s from both cameras were 11 ± 0.27%. However, these CWSI’s were not able to detect differences among irrigation treatments as compared to Ψs and gs.